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Recurrent Neural Networks for Narrowband Signal Detection in the Time-Frequency Domain

Published online by Cambridge University Press:  19 September 2017

David Brodrick ,
Douglas Taylor  and
Joachim Diederich
David Brodrick
Affiliation:
CSIRO Australia Telescope National Facility, Paul Wild Observatory, Locked Bag 194, Narrabri, NSW 2390, Australia, David.Brodrick@csiro.au
Douglas Taylor
Affiliation:
School of Computer Science and Software Engineering, Queensland University of Technology, GPO Box 2434, Brisbane, QLD 4001, Australia, di.taylor@qut.edu.au
Joachim Diederich
Affiliation:
School of Information Technology and Electrical Engineering, University of Queensland, St Lucia, QLD 4072, Australia, joachimd@itee.uq.edu.au

Abstract

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A recurrent neural network was trained to detect the time-frequency domain signature of narrowband radio signals against a background of astronomical noise. The objective was to investigate the use of recurrent networks for signal detection in the Search for Extra-Terrestrial Intelligence, though the problem is closely analogous to the detection of some classes of Radio Frequency Interference in radio astronomy.

Type
Search for Extraterrestrial Intelligence (SETI)
Information
Symposium - International Astronomical Union , Volume 213: Bioastronomy 2002: Life Among the stars , 2004 , pp. 483 - 486
Copyright
Copyright © Astronomical Society of the Pacific 2004 

References

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Taylor, D., Towsey, M., Finn, G., & Diederich, J. 1999, in Proceedings of the 6th International Conference on Bioastronomy Google Scholar